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"""
Using Dask for single-machine parallel computing
================================================
This example shows the simplest usage of the
`Dask <https://docs.dask.org/en/stable/>`_
backend on your local machine.
This is useful for prototyping a solution, to later be run on a truly
`distributed Dask cluster
<https://docs.dask.org/en/stable/deploying.html#distributed-computing>`_,
as the only change needed is the cluster class.
Another realistic usage scenario: combining dask code with joblib code,
for instance using dask for preprocessing data, and scikit-learn for
machine learning. In such a setting, it may be interesting to use
distributed as a backend scheduler for both dask and joblib, to
orchestrate the computation.
"""
###############################################################################
# Setup the distributed client
###############################################################################
from dask.distributed import Client, LocalCluster
# replace with whichever cluster class you're using
# https://docs.dask.org/en/stable/deploying.html#distributed-computing
cluster = LocalCluster()
# connect client to your cluster
client = Client(cluster)
# Monitor your computation with the Dask dashboard
print(client.dashboard_link)
###############################################################################
# Run parallel computation using dask.distributed
###############################################################################
import time
import joblib
def long_running_function(i):
time.sleep(0.1)
return i
###############################################################################
# The verbose messages below show that the backend is indeed the
# dask.distributed one
with joblib.parallel_config(backend="dask"):
joblib.Parallel(verbose=100)(
joblib.delayed(long_running_function)(i) for i in range(10)
)
###############################################################################
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